Building Bigger Applications with FastAPI

I share a few tips and tricks on how to build a clean and readable REST API for larger apps with FastAPI framework. You will learn how to split endpoints into separate Python scripts and assign tags with prefixes for all endpoints from the given router at once.

 

Merging GitHub Repo Template Changes

I explain how to merge changes from GitHub template repo. This allows developing infrastructure platform and products running on that platform independently. You can create new GitHub repo based on the template and later sync any changes from the template into that repo.

 

OpenAI GPT-3 API Overview

GPT-3 API review. I walk through a few examples and show how it works in OpenAI's playground. You will see how GPT-3 generates SQL statement from natural text, how it creates an outline for the essay, and generates recipe directions from food ingredients. There is an option to use GPT-3 API in your applications through REST interface.

 

Scale FastAPI on Kubernetes Pod

This video shows how to scale FastAPI REST endpoint running on Kubernetes Pod.

 

Python Numpy Array in ML Services

It can be not obvious how to pass Numpy array across separate services when running ML infra in separate containers. When data preparation service runs in a different container than training service. In this tutorial, I show how to convert Numpy to list and JSON to be able to send it through RabbitMQ message broker and consume it on the receiver side.

 

BIY Workflow with FastAPI, Python and Skipper

Build It Yourself. In this video, I explain how you can build workflow running with FastAPI REST generic endpoints. The best thing about it, Skipper architecture is modular and workflow runs in a separate Docker container. This means you can replace it with your own implementation if needed. I explain how workflow call is integrated into FastAPI logic and how the call is made to get the queue name from the workflow. Using this queue name and RabbitMQ message broker, event-based communication runs between containers.

 

MLOps: Extend Skipper ML Services

The goal of this video is to explain Skipper from MLOps user perspective, different blocks of Skipper and how they fit together. I show how a sample set of ML services works and how you could replace it or add your own service. Skipper engine is implemented with Python, but you could add service container implemented in any language. All runs on Kubernetes.